Early warning control and auxiliary diagnosis and treatment device for constructing intraoperative hypertension prediction model based on logistic regression algorithm and data mining technology

Abstract

The invention relates to an early warning control and auxiliary diagnosis and treatment device for constructing an intraoperative hypertension prediction model based on a logistic regression algorithm and a data mining technology, and belongs to the field of digital medical treatment. Comprising a data acquisition instrument used for acquiring historical medical record information and physiological parameter monitoring data of a patient and sending the data to a data processing module; the data processing module is used for selecting data corresponding to the influence characteristics of the intraoperative hypertension from the historical data of the patient, preprocessing and standardizing the data and sending the data to the model processing module; the model processing module inputs the standardized data into a trained intraoperative hypertension prediction model, outputs the probability of occurrence of intraoperative hypertension of the patient and a corresponding prediction label, and sends the probability and the corresponding prediction label to the front-end module; and the front-end module is used for displaying the early warning prompt information and managing and controlling the operation application. According to the device, the safety of an operation is improved by marking a patient who is predicted to have intraoperative hypertension, carrying out early warning prompt on a doctor and managing and controlling an operation application.

Description

Technical Field

[0001] The present invention relates to the field of digital medical technology, and in particular to an early warning control and auxiliary diagnosis and treatment device for constructing an intraoperative hypertension prediction model based on a logistic regression algorithm and data mining technology. Background Art

[0002] Intraoperative hypertension is a common clinical disease that has a significant impact on the patient's health status and surgical outcomes. Traditional predictions of intraoperative hypertension rely on the doctor's clinical experience, which is highly subjective, resulting in low efficiency and inaccurate prediction results. In addition, existing medical management systems usually do not mark patients who are likely to develop intraoperative hypertension, so there are no corresponding early warning and control measures, which reduces the safety of surgery.

[0003] Therefore, it is an urgent problem to provide an automated device to construct an intraoperative hypertension prediction model, mark patients who are predicted to develop intraoperative hypertension, provide early warning reminders to physicians, and manage surgical applications. Summary of the invention

[0004] In view of the above analysis, the present invention aims to provide an early warning control and auxiliary diagnosis and treatment device that constructs an intraoperative hypertension prediction model based on logistic regression algorithm and data mining technology, so as to solve the problem that the current prediction of intraoperative hypertension relies on the clinical experience of doctors, and the existing medical management system does not provide early warning and control for patients who are likely to develop intraoperative hypertension.

[0005] The present invention provides an early warning control and auxiliary diagnosis and treatment device for intraoperative hypertension, and the device comprises the following modules:

[0006] A data collector, used to collect the patient's historical medical record information through the electronic medical record system, collect the patient's historical physiological parameter monitoring data through the test result storage system or the scanning monitoring result, and send the historical medical record information and historical physiological parameter monitoring data to the data processing module;

[0007] A data processing module is used to obtain the influencing features of intraoperative hypertension, select data corresponding to the influencing features from the patient's historical medical information and historical physiological parameter monitoring data, perform preprocessing and standardize through a preset rule library, and send the standardized data to the model processing module;

[0008] A model processing module, used for running the trained intraoperative hypertension prediction model, inputting the standardized data into the trained intraoperative hypertension prediction model to predict whether the patient will develop intraoperative hypertension;

[0009] A memory device for storing the influencing features of intraoperative hypertension and a trained intraoperative hypertension prediction model;

[0010] The front-end module is used to display early warning information and control the surgery application when it is predicted that the patient will develop intraoperative hypertension.

[0011] Furthermore, the content of the early warning information includes the patient's age, BMI index, preoperative blood pressure, type of surgery, medical history information, and medication information, and the physician is required to click the "Confirm" button to ensure that the physician is aware of it.

[0012] Furthermore, the control over the surgical application includes: when the physician issues a surgical application, the front-end module displays a request for the physician to supplement relevant application materials and risk assessment materials, and the physician is allowed to continue submitting the surgical application only after the physician has supplemented the materials.

[0013] Furthermore, the front-end module is also used to provide the physician with a recommended diagnosis and treatment pathway for the patient when it is predicted that the patient will develop intraoperative hypertension.

[0014] Furthermore, the model processing module is also used to mark the patient as a patient at high risk of intraoperative hypertension in the medical management system when it is predicted that the patient will develop intraoperative hypertension.

[0015] Furthermore, the recommended diagnosis and treatment pathway for the patient is obtained in the following ways:

[0016] The data values ​​corresponding to the patient's "diagnosis result", "operation type", "affiliated department", and "gender" fields are retrieved in the database. Based on the search results, similar patients whose data values ​​corresponding to the "diagnosis result", "operation type", "affiliated department", and "gender" fields are the same as those of the patient and who are marked as high risk of intraoperative hypertension are retrieved in the database. The medical order information of each similar patient is obtained based on its unique identifier, and the medical order information of all similar patients is displayed as the recommended diagnosis and treatment path for the patient for the doctor to choose.

[0017] Furthermore, the trained intraoperative hypertension prediction model was obtained by the following method:

[0018] Collect historical medical record information and historical physiological parameter monitoring data of multiple patients through a data acquisition instrument, and pre-process the historical medical record information and historical physiological parameter monitoring data of the multiple patients; construct a rule base, and standardize and annotate the pre-processed data through the rule base to obtain first data;

[0019] Construct a prediction model for intraoperative hypertension;

[0020] Selecting the influencing feature and data values ​​corresponding to the target variable from the first data to establish a sample set;

[0021] The intraoperative hypertension prediction model is trained by using the sample set to obtain a trained intraoperative hypertension prediction model.

[0022] Furthermore, the step of training the intraoperative hypertension prediction model by using the sample set to obtain a trained intraoperative hypertension prediction model comprises:

[0023] Step S31, dividing the sample set into a training set and a test set;

[0024] Step S32: establishing a log-likelihood function based on the training set, and calculating the weight and bias of each influencing feature when the log-likelihood function is maximum;

[0025] Step S33, setting the calculation gradient of the weight and bias of each influencing feature, iteratively updating the weight and bias of each influencing feature through the calculation gradient, until the change of the weight and bias is less than a threshold or the maximum number of iterations is reached, and the iteration is stopped to obtain a trained intraoperative hypertension prediction model;

[0026] Step S34, input the test set into the trained intraoperative hypertension prediction model to obtain the predicted probability of intraoperative hypertension and the corresponding prediction label, calculate the accuracy and recall based on the corresponding prediction label and annotation label, if the accuracy and recall both reach the threshold, obtain the trained intraoperative hypertension prediction model; otherwise, expand the number of samples in the sample set or reduce the threshold corresponding to the predicted probability of intraoperative hypertension and return to step S31.

[0027] Furthermore, the intraoperative hypertension prediction model includes an input layer, a linear combination layer and an output layer; the input layer is used to receive the characteristic values ​​of the samples; the linear combination layer includes a linear combination unit and a mapping function, the linear combination unit is used to perform weighted summation on the input characteristic values, and the mapping function is used to map the output of the linear combination unit to the interval (0,1); the output layer is used to output the predicted probability of the patient developing intraoperative hypertension and the corresponding prediction label.

[0028] Furthermore, the predicted probability of intraoperative hypertension in patients was obtained by the following formula:

[0029]

[0030] Among them, P i represents the predicted probability of intraoperative hypertension in the ith patient; exp represents the exponential function; W ij is the weight of the jth influencing feature of the i-th patient; X ij is the data value corresponding to the jth influencing feature of the ith patient; n is the number of influencing features, that is, the sum of the first influencing feature, the second influencing feature and the target variable; W0 is the bias.

[0031] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0032] 1. The present invention collects information of surgical patients from multiple medical institutions, then searches for influencing factors of intraoperative hypertension from a large amount of data and trains an intraoperative hypertension prediction model based on the log-likelihood function, thereby improving the accuracy of intraoperative hypertension prediction.

[0033] 2. The present invention eliminates the problems of data redundancy, anomaly and missing by preprocessing the data; standardizes the preprocessed data by constructing a rule base, thereby unifying the terminology of data from different medical institutions, eliminating the problem of inconsistent terminology, improving the quality and availability of data, and improving the efficiency of data integration and analysis, providing a reliable foundation for the subsequent training and verification of the intraoperative hypertension model.

[0034] 3. The present invention automatically predicts and marks patients who will develop intraoperative hypertension through a trained intraoperative hypertension prediction model, thereby achieving early detection and prevention of intraoperative hypertension and improving work efficiency and treatment effects.

[0035] 4. When it is predicted that a patient will develop intraoperative hypertension, the present invention will mark the patient and issue an early warning to the physician and control the surgery application. Even if the physician is replaced later, the physician will be warned based on the mark, and relevant application materials and risk assessment materials will be supplemented in time during the surgery application process, thereby improving the safety of the surgery.

[0036] 5. The present invention finds similar patients to patients who are predicted to develop intraoperative hypertension, and provides the physician with the medical advice information of all similar patients as the recommended diagnosis and treatment path for the patient, thereby helping the physician to better formulate a treatment plan and make preoperative preparations, thereby improving the safety of the operation and the postoperative treatment effect.

[0037] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0039] Figure 1 It is a block diagram of an early warning control and auxiliary diagnosis and treatment device for intraoperative hypertension according to an embodiment of the present invention;

[0040] Figure 2 Schematic diagram of the structure of the intraoperative hypertension prediction model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0042] A specific embodiment of the present invention discloses an early warning control and auxiliary diagnosis and treatment device for intraoperative hypertension. Figure 1 As shown, the device includes the following modules:

[0043] A data collector, used to collect the patient's historical medical record information through the electronic medical record system, collect the patient's historical physiological parameter monitoring data through the test result storage system or the scanning monitoring result, and send the historical medical record information and historical physiological parameter monitoring data to the data processing module;

[0044] A data processing module is used to obtain the influencing features of intraoperative hypertension, select data corresponding to the influencing features from the patient's historical medical information and historical physiological parameter monitoring data, perform preprocessing and standardize through a preset rule library, and send the standardized data to the model processing module;

[0045] A model processing module, used for running the trained intraoperative hypertension prediction model, inputting the standardized data into the trained intraoperative hypertension prediction model to predict whether the patient will develop intraoperative hypertension;

[0046] A memory device for storing the influencing features of intraoperative hypertension and a trained intraoperative hypertension prediction model;

[0047] The front-end module is used to display early warning information and control the surgery application when it is predicted that the patient will develop intraoperative hypertension.

[0048] Furthermore, the data acquisition instrument includes an electronic medical record system interface, a test result storage system interface and a scanning device interface. The electronic medical record system interface is used to collect the patient's historical medical record information through the electronic medical record system, the test result storage system interface is used to collect the patient's historical physiological parameter monitoring data through medical equipment, and the scanning device interface is used to collect the patient's historical physiological parameter monitoring data by scanning the monitoring results with a scanning device.

[0049] Specifically, the electronic medical record system of a medical institution stores the historical medical record information of the patients, which includes the patient's personal information, medical information, examination / imaging information, medical advice information, diagnosis information, treatment information, medical history information, hospitalization records, and subsequent follow-up information.

[0050] Specifically, part of the patient's historical physiological parameter monitoring data is directly extracted from the test result storage system (such as electrocardiogram, blood pressure and other data), and the other part is collected by scanning paper monitoring results using OCR technology.

[0051] Specifically, the patient's medical history information refers to diseases related to intraoperative hypertension, including: cardiovascular system diseases, endocrine system diseases, kidney diseases, nervous system diseases, respiratory system diseases, and other system diseases; among them, cardiovascular system diseases include hypertension, heart disease, and heart failure, endocrine system diseases include diabetes and thyroid diseases, kidney diseases include chronic kidney disease and renal artery stenosis, nervous system diseases include cerebrovascular diseases and autonomic dysfunction, respiratory system diseases include chronic obstructive pulmonary disease and sleep apnea syndrome, and other system diseases include rheumatic immune diseases and blood system diseases. The patient's examination / imaging information includes renal function (serum creatinine, urea nitrogen, uric acid, glomerular filtration rate), blood lipids (total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol), blood sugar (fasting blood sugar, 2-hour postprandial blood sugar, glycosylated hemoglobin), electrolytes (sodium, potassium, chloride, calcium, phosphorus), inflammatory indicators (C-reactive protein, white blood cell count), and others (renin, angiotensin 2, aldosterone). The patient's physiological parameter monitoring data includes electrocardiogram, heart rate, blood pressure, blood oxygen saturation, body temperature, and respiratory rate. The physiological parameter monitoring data includes not only the monitoring data during the operation, but also the monitoring data obtained through examination.

[0052] It should be noted that intraoperative hypertension refers to hypertension that occurs in patients during surgery. Intraoperative hypertension is different from ordinary hypertension and is usually temporary. It may be caused by various factors such as surgical stimulation and the patient's medical history. Since intraoperative hypertension can easily lead to surgical complications, it is necessary to predict the probability of intraoperative hypertension in patients and take preventive measures to ensure the safety of the operation. It is understandable that the patient's personal information (such as age, BMI index, smoking, alcoholism, and sodium intake) will affect the probability of intraoperative hypertension. The patient's medical information (condition), examination / imaging information, and physiological parameter monitoring data directly determine the doctor's medical advice, diagnosis, and treatment information. The patient's medical history information, hospitalization records, and subsequent follow-up information determine the patient's basic physical condition.

[0053] Furthermore, the impact characteristics of intraoperative hypertension were obtained through the following steps:

[0054] Step S11, collecting historical medical record information and historical physiological parameter monitoring data of multiple patients through a data acquisition instrument, preprocessing the historical medical record information and historical physiological parameter monitoring data of the multiple patients; building a rule base, and standardizing and annotating the preprocessed data through the rule base to obtain first data;

[0055] Step S12: Select fields related to intraoperative hypertension from the first data as feature variables, select the "preoperative blood pressure" field as the target variable, and select influencing features from the feature variables based on correlation coefficient calculation and expert judgment.

[0056] Specifically, in step S11, the preprocessing of the historical medical record information and historical physiological parameter monitoring data of the multiple patients includes:

[0057] The historical medical record information and historical physiological parameter monitoring data of the multiple patients are stored as a data table according to the patients and the time of consultation. The data table includes fields, data values ​​corresponding to the fields, and value ranges of the fields; wherein the data of the same patient visiting the doctor on the same day is one record in the data table;

[0058] When the data value corresponding to a field exceeds the value range of the field, the record corresponding to the data value is deleted to remove the abnormal value;

[0059] By calculating the first similarity between the fields, the fields whose first similarity is greater than the corresponding threshold are merged into one field; by calculating the data fingerprint of each record, for each record, based on the data fingerprint of each record, the second similarity between the record and other records is calculated, and one record is selected from the records whose second similarity is greater than the corresponding threshold and the other records are deleted to remove redundant data;

[0060] When the data value corresponding to a field is missing, the data value is automatically inferred or supplemented manually.

[0061] Specifically, for each field in the data table, its value range is determined according to its meaning and actual situation. When the data value corresponding to a field exceeds the value range of the field, the record corresponding to the data value is deleted. The cosine similarity between the fields is calculated as the first similarity. When the fields with the first similarity greater than the corresponding threshold are merged into one field, if the merged fields have corresponding data values ​​in one record, the data with the most recent time is used as the data value corresponding to the merged field.

[0062] Further, the step of calculating the data fingerprint of each record, calculating the second similarity between the record and other records based on the data fingerprint of each record, selecting a record from the records whose second similarity is greater than a corresponding threshold and deleting other records to remove redundant data includes:

[0063] Step S21, setting the weight of each field;

[0064] Step S22: For each record, obtain the Hash value of the data value corresponding to each field of the record through the MD5 function, and obtain the data fingerprint of the record based on the Hash value of the data value corresponding to each field and its weight;

[0065] Step S23: For each record, calculate the Hamming distance between the data fingerprint of the record and the data fingerprints of other records to obtain a second similarity, and select the record with the least number of missing data values ​​corresponding to the field from the records whose second similarity is greater than the corresponding threshold; if there are multiple records with the least number of missing data values ​​corresponding to the field, select the record with the most recent time and delete the other records.

[0066] Specifically, in step S21, the weight of each field is set according to business experience, and the greater the weight, the more important the field is. The weight is a positive integer.

[0067] Specifically, in step S22, after the Hash value of the data value corresponding to each field is encoded (for example, 0 is encoded as -1), each bit of the Hash value is multiplied by its corresponding weight to obtain a weighted Hash value;

[0068] The weighted Hash value of each field is accumulated according to the corresponding bit to obtain the SimHash value of the record;

[0069] The SimHash value is converted into binary to obtain the data fingerprint of the record.

[0070] Specifically, in step S23, the second similarity calculation is performed by the following method:

[0071] R=1-(H(K1,K2) / W),

[0072] Among them, R is the similarity, K1 and K2 are two records, H(K1, K2) means calculating the Hamming distance between K1 and K2, and W is the number of bits of the data fingerprint.

[0073] It can be understood that the present application measures the similarity of two recorded data fingerprints by calculating their Hamming distance. The smaller the Hamming distance, the higher the similarity.

[0074] Specifically, when the data value corresponding to a field is missing, if the data value corresponding to the field can be inferred from the data values ​​corresponding to other fields, it is automatically inferred and supplemented according to the inference result; otherwise, the data value is supplemented manually.

[0075] For example, assume that the collected original data of the patient is as shown in Table 1, wherein the data value corresponding to the "sex" field of the patient in the records with IDs 1 and 3 is missing. The data value corresponding to the "sex" field can be inferred from the data value corresponding to the "menstrual history" field. The data value corresponding to the "menstrual history" field is 1, which represents menstrual history. Therefore, the data value corresponding to the "sex" field of the patient in the records with IDs 1 and 3 is automatically inferred to be "female", and the data value is supplemented with "female".

[0076] Table 1 Original data of patients collected

[0077]

[0078]

[0079] Furthermore, the construction of the rule base includes:

[0080] For the data value corresponding to each field, a mapping rule is defined based on a regular expression, and the data value is mapped into a standardized expression based on the mapping rule;

[0081] Building a list based on the standardized expression and corresponding fields;

[0082] The mapping rules and the list are stored in a database as a rule base.

[0083] It should be noted that the data value corresponding to each field is a term in the medical scenario. This application defines specific mapping rules for each of the above terms to map them into standardized expressions, where the disease name is mapped to the corresponding code according to the International Classification of Diseases coding table, the diagnosis result is mapped to the corresponding code according to the standard diagnosis coding table, and the type of surgery is mapped to the corresponding code according to the International Classification of Surgery coding table. These standardized expressions are verified, and a standardized term list is constructed based on the verified standardized expressions and the corresponding fields, and the mapping rules and standardized term list are stored in the database as a rule base.

[0084] For example, as shown in Table 2, mapping rules are defined for data values ​​corresponding to "sex", "disease name", "drug name", "diagnosis result" and "surgery type" based on regular expressions. Based on these mapping rules, sex is mapped to "M" or "F", stage 2 hypertension is mapped to "ICD-10:I10" according to the International Classification of Diseases coding table, aspirin is mapped to "aspirin", the diagnosis result of coronary artery atherosclerotic heart disease is mapped to "ICD-10:125.1" according to the standard diagnosis coding table, and appendectomy is mapped to "ICD-10-PCS:0DTJ4ZZ" according to the International Classification of Surgery coding table. A standardized term list is constructed based on the above standardized expressions and corresponding fields, as shown in Table 3.

[0085] Table 2 Mapping rules

[0086]

[0087]

[0088] Table 3 List of standardized terms

[0089] Fields Standardized expression illustrate gender M Indicates male gender F Indicates female Disease name ICD-10:I10 Indicates (essential) hypertension Drug Name aspirin English abbreviation: ASA Diagnosis ICD-10:125.1 Coronary atherosclerotic heart disease Type of surgery ICD-10-PCS:0DTJ4ZZ Appendectomy

[0090] It can be understood that the data value corresponding to the "Disease Name" field is a variety of specific diseases, and corresponding mapping rules need to be defined based on the terms of each disease. The above example only shows the mapping rules for common primary hypertension diseases.

[0091] Furthermore, the step of standardizing and labeling the preprocessed data through the rule base to obtain the first data includes:

[0092] Mapping the preprocessed data into a standardized expression through the rule base;

[0093] The mapped data is standardized in terms of date, time, value and unit according to the predetermined data format specification;

[0094] The data with unified format, that is, each record in the data table, is marked according to whether intraoperative hypertension occurs to obtain the first data.

[0095] Specifically, the predetermined data format specification includes date format, time format, decimal point digits, unified international units, etc. The label for intraoperative hypertension is 1, otherwise the label is 0.

[0096] It should be noted that the measurement units in different data sources need to be converted into unified international units, and unit conversions are performed as needed, such as converting blood pressure values ​​from mmHg to kPa, converting blood sugar values ​​from mg / dL to mmol / L, etc. Since the patient information collected from a single medical institution has limitations, this application collects information on surgical patients from multiple medical institutions, and then finds the influencing factors of intraoperative hypertension from a large amount of data and trains an intraoperative hypertension prediction model based on a large amount of data to improve the accuracy of intraoperative hypertension prediction. Since the collected patient information comes from different medical institutions and different systems of the same medical institution, and the term definitions and field definitions of each medical institution are different, the collected raw data not only has inconsistent terms, but also has redundancy, anomalies, and missing problems. This application eliminates the problems of data redundancy, anomalies, and missing by preprocessing the data; standardizes the preprocessed data by building a rule base, thereby unifying the terms of the data from different medical institutions, eliminating the problem of inconsistent terms, and improving the quality and availability of the data.

[0097] Specifically, in step S12, the selecting of influencing features from the feature variables according to correlation coefficient calculation and expert judgment includes:

[0098] The characteristic variable is divided into a first characteristic variable and a second characteristic variable, the correlation coefficient between the data value corresponding to the first characteristic variable and the data value corresponding to the target variable is calculated, and the first characteristic variable corresponding to the correlation coefficient greater than the threshold is selected as the first influencing feature; and the relevant characteristic variable is selected from the second characteristic variable as the second influencing feature according to expert judgment.

[0099] Specifically, the first characteristic variables include age, BMI index, smoking, alcoholism, sodium intake, blood sugar, blood lipids, history of hypertension, history of diabetes, family history of hypertension, and heart rate; the second characteristic variables include whether pressor drugs are used, whether hormonal drugs are used, whether non-steroidal anti-inflammatory drugs are used, whether oral contraceptives are used, whether antidepressants are used, and whether anti-tumor drugs are used.

[0100] Specifically, pressor drugs include dopamine, norepinephrine, and epinephrine; hormonal drugs include glucocorticoids such as dexamethasone, prednisone, and methylprednisolone; non-steroidal anti-inflammatory drugs include ibuprofen and naproxen; antidepressants include monoamine oxidase inhibitors and tricyclic antidepressants; and anti-tumor drugs include paclitaxel and cisplatin.

[0101] Furthermore, the correlation coefficient between the data value corresponding to the first characteristic variable and the data value corresponding to the target variable is calculated by the following formula:

[0102]

[0103] Among them, λ i is the correlation coefficient between the data value corresponding to the i-th first feature variable and the data value corresponding to the target variable, F i is the data value corresponding to the first characteristic variable of the i-th order, T is the data value corresponding to the target variable, cov(F i ,T) is F i Covariance with T, σF i F i is the standard deviation of , σT is the standard deviation of T.

[0104] It should be noted that each record in the first data is the corresponding data of a patient. It is necessary to select a certain number of data values ​​corresponding to the first characteristic variable from the first data according to the actual situation to calculate the above correlation coefficient. The larger the correlation coefficient, the greater the correlation between the first characteristic variable and intraoperative hypertension. The first influencing characteristics include age, BMI index, smoking, alcoholism, sodium intake, blood sugar, blood lipids, history of hypertension, and history of diabetes; the second influencing characteristics include whether to use pressor drugs, hormone drugs, non-steroidal anti-inflammatory drugs, and oral contraceptives.

[0105] Furthermore, the data processing module includes a preprocessing unit and a standardization unit, wherein the preprocessing unit is used to preprocess the data and send the preprocessed data to the standardization unit, and the standardization unit is used to standardize the preprocessed data through a preset rule base and send the standardized data to the model processing module.

[0106] It should be noted that the preprocessing unit performs preprocessing by the method in step S11, and the standardization unit performs standardization by the method in step S11.

[0107] Furthermore, the trained intraoperative hypertension prediction model was obtained by the following method:

[0108] Construct a prediction model for intraoperative hypertension;

[0109] Selecting the influencing feature and data values ​​corresponding to the target variable from the first data to establish a sample set;

[0110] The intraoperative hypertension prediction model is trained by using the sample set to obtain a trained intraoperative hypertension prediction model.

[0111] Specifically, the first influencing feature, the second influencing feature, and data values ​​corresponding to the target variable are selected from the first data to establish a sample set. Figure 2As shown, the intraoperative hypertension prediction model includes an input layer, a linear combination layer and an output layer; the input layer is used to receive the characteristic value of the sample; the linear combination layer includes a linear combination unit and a mapping function, the linear combination unit is used to perform weighted summation on the input characteristic value, and the mapping function is used to map the output of the linear combination unit to the interval (0,1); the output layer is used to output the predicted probability of the patient developing intraoperative hypertension and the corresponding prediction label.

[0112] Furthermore, the predicted probability of intraoperative hypertension in patients was obtained by the following formula:

[0113]

[0114] Among them, P i represents the predicted probability of intraoperative hypertension in the ith patient; exp represents the exponential function; W ij is the weight of the jth influencing feature of the i-th patient; X ij is the data value corresponding to the jth influencing feature of the ith patient; n is the number of influencing features, that is, the sum of the first influencing feature, the second influencing feature and the target variable; W0 is the bias.

[0115] It should be noted that when the predicted probability of a patient developing intraoperative hypertension is greater than a preset threshold (for example, 0.5), the corresponding prediction label of the patient is set to 1, otherwise it is set to 0; when the patient's prediction label is 1, it is predicted that the patient will develop intraoperative hypertension.

[0116] Furthermore, the expression of the i-th sample is: D i =(X i1 ,X i2 ,......X in ,Y i ), where X in represents the data value corresponding to the nth influencing feature of the i-th sample, where n is the number of influencing features, i.e., the sum of the first influencing feature, the second influencing feature, and the target variable; Y i Represents the annotation label of the i-th sample.

[0117] Furthermore, the step of training the intraoperative hypertension prediction model by using the sample set to obtain a trained intraoperative hypertension prediction model comprises:

[0118] Step S31, dividing the sample set into a training set and a test set;

[0119] Step S32: establishing a log-likelihood function based on the training set, and calculating the weight and bias of each influencing feature when the log-likelihood function is maximum;

[0120] Step S33, setting the calculation gradient of the weight and bias of each influencing feature, iteratively updating the weight and bias of each influencing feature through the calculation gradient, until the change of the weight and bias is less than a threshold or the maximum number of iterations is reached, and the iteration is stopped to obtain a trained intraoperative hypertension prediction model;

[0121] Step S34, input the test set into the trained intraoperative hypertension prediction model to obtain the predicted probability of intraoperative hypertension and the corresponding prediction label, calculate the accuracy and recall based on the corresponding prediction label and annotation label, if the accuracy and recall both reach the threshold, obtain the trained intraoperative hypertension prediction model; otherwise, expand the number of samples in the sample set or reduce the threshold corresponding to the predicted probability of intraoperative hypertension and return to step S31.

[0122] Specifically, in step S31, the sample set is divided into a training set and a test set at a ratio of 8:2.

[0123] Specifically, in step S32, the expression of the log-likelihood function is:

[0124]

[0125] Among them, l(W ij ,W0) represents the log-likelihood function, ln represents the logarithm with base e, Y i represents the annotation label of the i-th patient.

[0126] Find l(W ij ,W0) is the maximum weight W ij and W0, thereby obtaining the weight and bias of each influencing feature.

[0127] Specifically, in step S33, the calculation gradient of the weight of each influencing feature is set by the following formula:

[0128]

[0129] in, represents the predicted label of the ith patient, and m is the number of samples in the training set.

[0130] The calculation gradient of the bias is set by the following formula:

[0131]

[0132] Furthermore, the weight of each influencing feature is updated by the following formula:

[0133]

[0134] Among them, W ij (t+1)represents the updated weight of the jth influencing feature of the ith patient, W ij (t) represents the weight of the jth influencing feature of the current i-th patient, and α is a constant representing the learning rate.

[0135] Update the bias using the following formula:

[0136]

[0137] Among them, W0 (t+1) represents the updated bias of the ith patient, W0 (t) represents the bias of the current i-th patient.

[0138] Preferably, α is set to 0.01, and the change in weight and bias is set to be less than the threshold value 10 -6 Or the iteration stops when the maximum number of iterations, 1000, is reached.

[0139] It should be noted that when the iteration is stopped, the current weights and biases of the various influencing features are recorded, so as to obtain a trained intraoperative hypertension prediction model corresponding to the weights and biases.

[0140] Specifically, in step S34, the accuracy is calculated by the following formula:

[0141]

[0142] Among them, TP represents the number of correct predictions of intraoperative hypertension, and FP represents the number of incorrect predictions of intraoperative hypertension.

[0143] The recall rate is calculated by the following formula:

[0144]

[0145] Among them, TN represents the number of cases in which intraoperative hypertension was correctly predicted, and FN represents the number of cases in which intraoperative hypertension was not predicted but actually occurred.

[0146] It should be noted that the accuracy reflects the proportion of samples with correct predictions of intraoperative hypertension to the total number of samples, and the recall rate reflects the proportion of samples with correct predictions of intraoperative hypertension to all samples that actually experience intraoperative hypertension.

[0147] Furthermore, the threshold corresponding to expanding the number of samples in the sample set or reducing the predicted probability of intraoperative hypertension includes:

[0148] If the accuracy rate does not reach the threshold, the number of samples in the sample set is expanded, thereby expanding the number of samples in the training set; if the recall rate does not reach the threshold, the threshold corresponding to the predicted probability of intraoperative hypertension is lowered.

[0149] It can be understood that when the recall rate does not reach the threshold, it means that a large proportion of samples that actually experience intraoperative hypertension are not correctly predicted to have intraoperative hypertension. Therefore, the threshold corresponding to the predicted probability of intraoperative hypertension should be lowered so that the predicted labels of more samples are set to 1, that is, the number of samples predicted to have intraoperative hypertension is increased.

[0150] Furthermore, the model processing module is also used to mark the patient as a patient at high risk of intraoperative hypertension in the medical management system when it is predicted that the patient will develop intraoperative hypertension.

[0151] Furthermore, the content of the warning prompt information includes the patient's age, BMI index, preoperative blood pressure, surgery type, medical history information, and medication information, and the physician is required to click the "Confirm" button to ensure that the physician is aware of it;

[0152] The control over surgical applications includes: when a physician issues a surgical application, the front-end module displays a request for the physician to supplement relevant application materials and risk assessment materials, and the physician is allowed to continue submitting the surgical application only after the physician has supplemented the materials.

[0153] Furthermore, the front-end module is also used to provide the physician with a recommended diagnosis and treatment pathway for the patient when it is predicted that the patient will develop intraoperative hypertension.

[0154] Specifically, the data values ​​corresponding to the patient's "diagnosis result", "surgery type", "affiliated department", and "gender" fields are retrieved in the database, and based on the search results, similar patients whose data values ​​corresponding to the "diagnosis result", "surgery type", "affiliated department", and "gender" fields are the same as those of the patient and who are marked as at high risk of intraoperative hypertension are retrieved in the database, and the medical order information of each similar patient is obtained based on its unique identifier (ID), and the medical order information of all similar patients is displayed as the recommended diagnosis and treatment path for the patient for the doctor to choose.

[0155] It should be noted that medical order information includes examination, surgery, drug information, nursing measures, and dietary restrictions prescribed by the physician. The above-mentioned recommended treatment pathways assist physicians in providing targeted diagnosis and treatment, thereby improving the effectiveness of diagnosis and treatment.

[0156] It can be understood that the present application marks patients who are predicted to develop intraoperative hypertension and issues early warning reminders. Even if the physician is subsequently changed, the physician will be warned for the patient, thereby improving the safety of medical practices.

[0157] Compared with the prior art, the early warning control, auxiliary diagnosis and treatment device and the intraoperative hypertension early warning control and auxiliary diagnosis and treatment system provided by the present invention, which use the logistic regression algorithm and data mining technology to construct an intraoperative hypertension prediction model, have the following beneficial effects:

[0158] 1. The present invention collects information of surgical patients from multiple medical institutions, then searches for influencing factors of intraoperative hypertension from a large amount of data and trains an intraoperative hypertension prediction model based on the log-likelihood function, thereby improving the accuracy of intraoperative hypertension prediction.

[0159] 2. The present invention eliminates the problems of data redundancy, anomaly and missing by preprocessing the data; standardizes the preprocessed data by constructing a rule base, thereby unifying the terminology of data from different medical institutions, eliminating the problem of inconsistent terminology, improving the quality and availability of data, and improving the efficiency of data integration and analysis, providing a reliable foundation for the subsequent training and verification of the intraoperative hypertension model.

[0160] 3. The present invention automatically predicts and marks patients who will develop intraoperative hypertension through a trained intraoperative hypertension prediction model, thereby achieving early detection and prevention of intraoperative hypertension and improving work efficiency and treatment effects.

[0161] 4. When it is predicted that a patient will develop intraoperative hypertension, the present invention will mark the patient and issue an early warning to the physician and control the surgery application. Even if the physician is replaced later, the physician will be warned based on the mark, and relevant application materials and risk assessment materials will be supplemented in time during the surgery application process, thereby improving the safety of the surgery.

[0162] 5. The present invention finds similar patients to patients who are predicted to develop intraoperative hypertension, and provides the physician with the medical advice information of all similar patients as the recommended diagnosis and treatment path for the patient, thereby helping the physician to better formulate a treatment plan and make preoperative preparations, thereby improving the safety of the operation and the postoperative treatment effect.

[0163] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0164] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A device for early warning control and auxiliary diagnosis and treatment of hypertension during surgery, characterized in that: The device comprises the following modules: A data collector, used to collect the patient's historical medical record information through the electronic medical record system, collect the patient's historical physiological parameter monitoring data through the test result storage system or the scanning monitoring result, and send the historical medical record information and historical physiological parameter monitoring data to the data processing module; A data processing module is used to obtain the influencing features of intraoperative hypertension, select data corresponding to the influencing features from the patient's historical medical information and historical physiological parameter monitoring data, perform preprocessing and standardize through a preset rule library, and send the standardized data to the model processing module; A model processing module, used for running the trained intraoperative hypertension prediction model, inputting the standardized data into the trained intraoperative hypertension prediction model to predict whether the patient will develop intraoperative hypertension; A memory device for storing the influencing features of intraoperative hypertension and a trained intraoperative hypertension prediction model; The front-end module is used to display early warning information and control the surgery application when it is predicted that the patient will develop intraoperative hypertension.

2. The early warning control and auxiliary diagnosis and treatment device for intraoperative hypertension according to claim 1, characterized in that: The content of the warning prompt information includes the patient's age, BMI index, preoperative blood pressure, type of surgery, medical history information, and medication information. The doctor needs to click the "Confirm" button to ensure that the doctor is aware of it.

3. The early warning control and auxiliary diagnosis and treatment device for intraoperative hypertension according to claim 1, characterized in that: The control over surgical applications includes: when a physician issues a surgical application, the front-end module displays a request for the physician to supplement relevant application materials and risk assessment materials, and the physician is allowed to continue submitting the surgical application only after the physician has supplemented the materials.

4. The early warning control and auxiliary diagnosis and treatment device for intraoperative hypertension according to claim 1, characterized in that: The front-end module is also used to provide the physician with a recommended diagnosis and treatment pathway for the patient when it is predicted that the patient will develop intraoperative hypertension.

5. The early warning control and auxiliary diagnosis and treatment device for intraoperative hypertension according to claim 4, characterized in that: The model processing module is also used to mark the patient as a patient at high risk of intraoperative hypertension in the medical management system when it is predicted that the patient will develop intraoperative hypertension.

6. The early warning control and auxiliary diagnosis and treatment device for intraoperative hypertension according to claim 5, characterized in that: Obtain the recommended treatment pathway for the patient through the following methods: The data values ​​corresponding to the patient's "diagnosis result", "surgery type", "affiliated department", and "gender" fields are retrieved in the database. Based on the search results, similar patients whose data values ​​corresponding to the "diagnosis result", "surgery type", "affiliated department", and "gender" fields are the same as those of the patient and who are marked as high risk of intraoperative hypertension are retrieved in the database. The medical order information of each similar patient is obtained based on its unique identifier, and the medical order information of all similar patients is displayed as the recommended diagnosis and treatment path for the patient for the doctor to choose.

7. The early warning control and auxiliary diagnosis and treatment device for intraoperative hypertension according to claim 1, characterized in that: The trained intraoperative hypertension prediction model was obtained by the following method: Collect historical medical record information and historical physiological parameter monitoring data of multiple patients through a data acquisition instrument, and pre-process the historical medical record information and historical physiological parameter monitoring data of the multiple patients; construct a rule base, and standardize and annotate the pre-processed data through the rule base to obtain first data; Construct a prediction model for intraoperative hypertension; Selecting the influencing feature and data values ​​corresponding to the target variable from the first data to establish a sample set; The intraoperative hypertension prediction model is trained by using the sample set to obtain a trained intraoperative hypertension prediction model.

8. The early warning control and auxiliary diagnosis and treatment device for intraoperative hypertension according to claim 7, characterized in that: The step of training the intraoperative hypertension prediction model by using the sample set to obtain a trained intraoperative hypertension prediction model comprises: Step S31, dividing the sample set into a training set and a test set; Step S32: establishing a log-likelihood function based on the training set, and calculating the weight and bias of each influencing feature when the log-likelihood function is maximum; Step S33, setting the calculation gradient of the weight and bias of each influencing feature, iteratively updating the weight and bias of each influencing feature through the calculation gradient, until the change of the weight and bias is less than a threshold or the maximum number of iterations is reached, and the iteration is stopped to obtain a trained intraoperative hypertension prediction model; Step S34, input the test set into the trained intraoperative hypertension prediction model to obtain the predicted probability of intraoperative hypertension and the corresponding prediction label, calculate the accuracy and recall based on the corresponding prediction label and annotation label, if the accuracy and recall both reach the threshold, obtain the trained intraoperative hypertension prediction model; otherwise, expand the number of samples in the sample set or reduce the threshold corresponding to the predicted probability of intraoperative hypertension and return to step S31.

9. The early warning control and auxiliary diagnosis and treatment device for intraoperative hypertension according to claim 7, characterized in that: The intraoperative hypertension prediction model includes an input layer, a linear combination layer and an output layer; the input layer is used to receive the characteristic values ​​of the samples; the linear combination layer includes a linear combination unit and a mapping function, the linear combination unit is used to perform weighted summation on the input characteristic values, and the mapping function is used to map the output of the linear combination unit to the interval (0,1); the output layer is used to output the predicted probability of the patient developing intraoperative hypertension and the corresponding prediction label.

10. The early warning control and auxiliary diagnosis and treatment device for intraoperative hypertension according to claim 9, characterized in that: The predicted probability of intraoperative hypertension in patients was obtained by the following formula: Among them, P i represents the predicted probability of intraoperative hypertension in the ith patient; exp represents the exponential function; W ij is the weight of the jth influencing feature of the i-th patient; X ij is the data value corresponding to the jth influencing feature of the ith patient; n is the number of influencing features, that is, the sum of the first influencing feature, the second influencing feature and the target variable; W0 is the bias.

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